Bayesian Model Selection for Beta Autoregressive Processes

We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the class of conditionally linear processes. These processes are particularly suitable for forecasting purposes, but are difficult to estimate due to the constraints on the parameter space. We provide a full Bayesian approach to the estimation and include the parameter restrictions in the inference problem by a suitable specification of the prior distributions. Moreover in a Bayesian framework parameter estimation and model choice can be solved simultaneously. In particular we suggest a Markov-Chain Monte Carlo (MCMC) procedure based on a Metropolis-Hastings within Gibbs algorithm and solve the model selection problem following a reversible jump MCMC approach

Canaryseed (Phalaris canariensis L.) accessions from nineteen countries show useful genetic variation for agronomic traits

Fifty-seven accessions of canaryseed (47 populations and 10 cultivars) from 19 countries were evaluated for agronomic traits in four field trials sown over 3 yr in the province of Buenos Aires, Argentina. Genetic variation was found for all traits scored: grain yield and its components (grain weight, grain number per square meter, grain number per head and head number per square meter), harvest index, percent lodging, and phenological characters (emergence to heading, emergence to harvest maturity and heading to harvest maturity). Although genotype×environment interaction was observed for all traits, the additive differences between accessions were sufficient to enable promising breeding materials to be identified. Accessions superior in performance to the local Argentinean population, which in general gave values close to the overall mean of the accessions evaluated, were identified. For example, a population of Moroccan origin gave good yield associated with elevated values of the highly heritable character grain weight, rather than with the more commonly observed grain number per square meter. This population was also of relatively short stature and resistant to lodging, and, although it performed best when sown within the normal sowing date, tolerated late sowing fairly well. Other accessions were also observed with high grain weight, a useful characteristic in itself, since large grains are desirable from a quality point of view. Regarding phenology, the accessions showed a range of 160 degree days (8 calendar days in our conditions) in maturity, which, while not large in magnitude, may be of some utility in crop rotation management. Some accessions were well adapted to late sowing. Grain yield in general was strongly correlated with grain number per square meter. Principal components analysis (PCA) carried out for all characteristics provided indications of accessions combining useful characteristics and identified three components that explained approximately 70% of the phenotypic variation. Furthermore, a second PCA plus regression showed that approximately 60% of the variation in grain yield could be explained by a component associated with harvest index and grain number per square meter. Pointers were provided to possible future breeding targets

Co-evaluation of climate services. A case study for hydropower generation

Climate services are attracting growing attention and interest as instruments to promote climate change adaptation. The transparent assessment of the potential value brought by the services can play a major role. It can foster the commitment of the user towards a co-generation process increasingly central to climate services creation, can provide developers important information to better tailor the service to the user needs, and can finally increase recognition of the value of the service boosting confidence and trust in the tool. This study presents and then demonstrates the applicability of an evaluation methodology based on the Bayesian framework derived from the information value theory. The specific case study is the Smart Climate Hydropower Tool (SCHT), a climate service designed to support management decisions in hydropower generation. The service uses freely available seasonal forecasts and machine learning algorithms to predict incoming discharge to hydropower reservoirs. The user is ENEL Green Power Italy, and the testing environments are two water basins in Colombia. The study defines the expected value of perfect information, the expected value of the information currently used by the hydropower producer and the expected value of the service information. It then discusses pros and cons of the applicability of the method

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Smart climate hydropower tool: A machine-learning seasonal forecasting climate service to support cost–benefit analysis of reservoir management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Large-scale response of the Eastern Mediterranean thermohaline circulation to African monsoon intensification during sapropel S1 formation

This study was supported by Shell International Exploration and Production Inc. We thank the R/V URANIA crew for at sea assistance. This is the ISMAR contribution n. 1914. We thank Dr. L. Capotondi and Dr. L. Vigliotti for their constructive comments on the first draft of the manuscript. We also thank Dr. Daria Pasqual (University of Padova, Dept. of Geosciences) for her assistance in XRF analyses. We thank two anonymous reviewers and the Editor H. Bauch for their constructive comments. We also acknowledge Prof. Gerhard Schmiedl (Universität Hamburg) and Associate Prof. Syee Weldeab (Earth Science, UC Santa Barbara) for providing published data used in this study.Peer reviewedPostprin